Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations365788
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.3 MiB
Average record size in memory207.1 B

Variable types

Numeric15
Categorical4

Alerts

ADV_S is highly overall correlated with DATA_S and 3 other fieldsHigh correlation
DATA_R is highly overall correlated with Data_Sent_To_BS and 5 other fieldsHigh correlation
DATA_S is highly overall correlated with ADV_S and 3 other fieldsHigh correlation
Data_Sent_To_BS is highly overall correlated with DATA_R and 3 other fieldsHigh correlation
Dist_To_CH is highly overall correlated with DATA_R and 3 other fieldsHigh correlation
Is_CH is highly overall correlated with JOIN_S and 2 other fieldsHigh correlation
JOIN_R is highly overall correlated with ADV_S and 1 other fieldsHigh correlation
JOIN_S is highly overall correlated with DATA_R and 9 other fieldsHigh correlation
SCH_R is highly overall correlated with DATA_R and 8 other fieldsHigh correlation
SCH_S is highly overall correlated with ADV_S and 1 other fieldsHigh correlation
Time is highly overall correlated with JOIN_S and 2 other fieldsHigh correlation
dist_CH_To_BS is highly overall correlated with DATA_R and 5 other fieldsHigh correlation
id is highly overall correlated with JOIN_S and 3 other fieldsHigh correlation
send_code is highly overall correlated with DATA_R and 5 other fieldsHigh correlation
who CH is highly overall correlated with JOIN_S and 3 other fieldsHigh correlation
Attack type is highly overall correlated with Is_CH and 2 other fieldsHigh correlation
Rank is highly overall correlated with ADV_S and 1 other fieldsHigh correlation
Attack type is highly imbalanced (73.6%) Imbalance
Expaned Energy is highly skewed (γ1 = 25.69934767) Skewed
Dist_To_CH has 77990 (21.3%) zeros Zeros
ADV_S has 323400 (88.4%) zeros Zeros
ADV_R has 30155 (8.2%) zeros Zeros
JOIN_R has 347118 (94.9%) zeros Zeros
SCH_S has 347130 (94.9%) zeros Zeros
Rank has 70737 (19.3%) zeros Zeros
DATA_S has 58829 (16.1%) zeros Zeros
DATA_R has 314028 (85.8%) zeros Zeros
Data_Sent_To_BS has 303545 (83.0%) zeros Zeros
dist_CH_To_BS has 303545 (83.0%) zeros Zeros
send_code has 77949 (21.3%) zeros Zeros

Reproduction

Analysis started2025-05-12 17:13:32.231630
Analysis finished2025-05-12 17:15:30.837923
Duration1 minute and 58.61 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation 

Distinct11120
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265027.89
Minimum101000
Maximum3402096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:31.296018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile102035
Q1107096
median116072
Q3214072
95-th percentile804098
Maximum3402096
Range3301096
Interquartile range (IQR)106976

Descriptive statistics

Standard deviation363296.1
Coefficient of variation (CV)1.3707844
Kurtosis23.490804
Mean265027.89
Median Absolute Deviation (MAD)13047
Skewness4.3864773
Sum9.6944023 × 1010
Variance1.3198406 × 1011
MonotonicityNot monotonic
2025-05-12T22:15:31.739314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101041 136
 
< 0.1%
101000 135
 
< 0.1%
101065 135
 
< 0.1%
101075 135
 
< 0.1%
101074 135
 
< 0.1%
101073 135
 
< 0.1%
101072 135
 
< 0.1%
101071 135
 
< 0.1%
101070 135
 
< 0.1%
101069 135
 
< 0.1%
Other values (11110) 364437
99.6%
ValueCountFrequency (%)
101000 135
< 0.1%
101001 135
< 0.1%
101002 135
< 0.1%
101003 135
< 0.1%
101004 135
< 0.1%
101005 135
< 0.1%
101006 135
< 0.1%
101007 135
< 0.1%
101008 135
< 0.1%
101009 135
< 0.1%
ValueCountFrequency (%)
3402096 1
< 0.1%
3402088 1
< 0.1%
3402073 1
< 0.1%
3402069 1
< 0.1%
3402063 1
< 0.1%
3402054 1
< 0.1%
3402034 1
< 0.1%
3402016 1
< 0.1%
3402009 1
< 0.1%
3402001 1
< 0.1%

Time
Real number (ℝ)

High correlation 

Distinct196
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1062.5201
Minimum50
Maximum3600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:32.090605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile103
Q1353
median803
Q31503
95-th percentile3053
Maximum3600
Range3550
Interquartile range (IQR)1150

Descriptive statistics

Standard deviation896.35523
Coefficient of variation (CV)0.84361247
Kurtosis0.30756442
Mean1062.5201
Median Absolute Deviation (MAD)500
Skewness1.1037821
Sum3.8865711 × 108
Variance803452.69
MonotonicityNot monotonic
2025-05-12T22:15:32.464932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 13201
 
3.6%
103 13101
 
3.6%
253 13065
 
3.6%
153 12805
 
3.5%
203 12701
 
3.5%
303 12649
 
3.5%
353 12356
 
3.4%
403 11702
 
3.2%
453 11297
 
3.1%
503 11177
 
3.1%
Other values (186) 241734
66.1%
ValueCountFrequency (%)
50 300
 
0.1%
53 13201
3.6%
100 300
 
0.1%
103 13101
3.6%
150 300
 
0.1%
153 12805
3.5%
200 300
 
0.1%
203 12701
3.5%
250 300
 
0.1%
253 13065
3.6%
ValueCountFrequency (%)
3600 10
 
< 0.1%
3553 1687
0.5%
3550 10
 
< 0.1%
3503 1738
0.5%
3500 10
 
< 0.1%
3473 1
 
< 0.1%
3453 1728
0.5%
3450 10
 
< 0.1%
3403 1772
0.5%
3400 10
 
< 0.1%

Is_CH
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
0
323400 
1
42388 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters365788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Length

2025-05-12T22:15:32.782537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T22:15:33.067258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

who CH
Real number (ℝ)

High correlation 

Distinct7088
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265038.68
Minimum101000
Maximum3402100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:33.359889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile102034
Q1107100
median116073
Q3214089
95-th percentile804100
Maximum3402100
Range3301100
Interquartile range (IQR)106989

Descriptive statistics

Standard deviation363308.73
Coefficient of variation (CV)1.3707762
Kurtosis23.489083
Mean265038.68
Median Absolute Deviation (MAD)13048
Skewness4.3863001
Sum9.694797 × 1010
Variance1.3199323 × 1011
MonotonicityNot monotonic
2025-05-12T22:15:33.725567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202100 3422
 
0.9%
201100 1854
 
0.5%
301100 1751
 
0.5%
203100 1599
 
0.4%
501100 1519
 
0.4%
601100 1475
 
0.4%
402100 1474
 
0.4%
502100 1456
 
0.4%
401100 1449
 
0.4%
602100 1415
 
0.4%
Other values (7078) 348374
95.2%
ValueCountFrequency (%)
101000 196
0.1%
101001 374
0.1%
101002 104
 
< 0.1%
101003 277
0.1%
101005 293
0.1%
101006 255
0.1%
101007 134
 
< 0.1%
101008 78
 
< 0.1%
101009 185
0.1%
101010 93
 
< 0.1%
ValueCountFrequency (%)
3402100 10
 
< 0.1%
3401100 26
 
< 0.1%
3302100 58
< 0.1%
3301100 81
< 0.1%
3301096 1
 
< 0.1%
3301054 1
 
< 0.1%
3301034 1
 
< 0.1%
3301001 1
 
< 0.1%
3202100 84
< 0.1%
3202096 1
 
< 0.1%

Dist_To_CH
Real number (ℝ)

High correlation  Zeros 

Distinct13956
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.786868
Minimum0
Maximum214.27462
Zeros77990
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:34.074830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.74685
median18.56966
Q333.9213
95-th percentile62.54398
Maximum214.27462
Range214.27462
Interquartile range (IQR)28.17445

Descriptive statistics

Standard deviation21.944243
Coefficient of variation (CV)0.96302145
Kurtosis5.5162019
Mean22.786868
Median Absolute Deviation (MAD)14.28935
Skewness1.6674018
Sum8335162.9
Variance481.54979
MonotonicityNot monotonic
2025-05-12T22:15:34.419595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77990
 
21.3%
5.96867 357
 
0.1%
8.75269 298
 
0.1%
8.97549 292
 
0.1%
3.96705 273
 
0.1%
6.555 271
 
0.1%
8.67263 266
 
0.1%
1.1225 266
 
0.1%
3.07338 260
 
0.1%
7.04221 256
 
0.1%
Other values (13946) 285259
78.0%
ValueCountFrequency (%)
0 77990
21.3%
0.40092 126
 
< 0.1%
0.48993 81
 
< 0.1%
0.66713 85
 
< 0.1%
0.67335 95
 
< 0.1%
0.80238 43
 
< 0.1%
0.83439 109
 
< 0.1%
0.88798 251
 
0.1%
1.1225 266
 
0.1%
1.23076 237
 
0.1%
ValueCountFrequency (%)
214.27462 25
< 0.1%
213.85247 20
< 0.1%
206.32935 9
 
< 0.1%
205.22083 9
 
< 0.1%
203.79135 8
 
< 0.1%
202.98739 7
 
< 0.1%
190.43411 5
 
< 0.1%
186.94848 7
 
< 0.1%
184.31794 6
 
< 0.1%
183.55017 7
 
< 0.1%

ADV_S
Real number (ℝ)

High correlation  Zeros 

Distinct85
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26713014
Minimum0
Maximum97
Zeros323400
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:34.894088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0703395
Coefficient of variation (CV)7.7503029
Kurtosis547.0163
Mean0.26713014
Median Absolute Deviation (MAD)0
Skewness19.543366
Sum97713
Variance4.2863057
MonotonicityNot monotonic
2025-05-12T22:15:35.259585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 323400
88.4%
1 38884
 
10.6%
6 194
 
0.1%
10 188
 
0.1%
17 184
 
0.1%
18 183
 
0.1%
16 177
 
< 0.1%
13 175
 
< 0.1%
11 175
 
< 0.1%
19 174
 
< 0.1%
Other values (75) 2054
 
0.6%
ValueCountFrequency (%)
0 323400
88.4%
1 38884
 
10.6%
3 121
 
< 0.1%
4 119
 
< 0.1%
5 121
 
< 0.1%
6 194
 
0.1%
7 111
 
< 0.1%
8 116
 
< 0.1%
9 103
 
< 0.1%
10 188
 
0.1%
ValueCountFrequency (%)
97 2
 
< 0.1%
96 3
< 0.1%
93 2
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
90 1
 
< 0.1%
88 2
 
< 0.1%
87 1
 
< 0.1%
84 1
 
< 0.1%
83 5
< 0.1%

ADV_R
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9806719
Minimum0
Maximum117
Zeros30155
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:35.659934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q37
95-th percentile27
Maximum117
Range117
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.0370171
Coefficient of variation (CV)1.0080716
Kurtosis8.5009254
Mean6.9806719
Median Absolute Deviation (MAD)2
Skewness2.3047779
Sum2553446
Variance49.519609
MonotonicityNot monotonic
2025-05-12T22:15:35.978949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 53069
14.5%
5 51475
14.1%
6 42430
11.6%
3 37532
10.3%
7 34155
9.3%
0 30155
8.2%
2 23314
6.4%
8 20640
 
5.6%
9 13413
 
3.7%
28 10860
 
3.0%
Other values (21) 48745
13.3%
ValueCountFrequency (%)
0 30155
8.2%
1 8077
 
2.2%
2 23314
6.4%
3 37532
10.3%
4 53069
14.5%
5 51475
14.1%
6 42430
11.6%
7 34155
9.3%
8 20640
 
5.6%
9 13413
 
3.7%
ValueCountFrequency (%)
117 34
 
< 0.1%
29 7
 
< 0.1%
28 10860
3.0%
27 7732
2.1%
26 7877
2.2%
25 4517
1.2%
24 2051
 
0.6%
23 894
 
0.2%
22 729
 
0.2%
21 814
 
0.2%

JOIN_S
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
1
287839 
0
77949 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters365788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Length

2025-05-12T22:15:36.304993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T22:15:36.607113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring characters

ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

JOIN_R
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74639682
Minimum0
Maximum124
Zeros347118
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:36.883839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum124
Range124
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7140532
Coefficient of variation (CV)6.3157466
Kurtosis123.06007
Mean0.74639682
Median Absolute Deviation (MAD)0
Skewness9.7145762
Sum273023
Variance22.222297
MonotonicityNot monotonic
2025-05-12T22:15:37.282935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347118
94.9%
1 1745
 
0.5%
2 1278
 
0.3%
3 1159
 
0.3%
4 1033
 
0.3%
5 935
 
0.3%
6 869
 
0.2%
7 709
 
0.2%
8 692
 
0.2%
9 657
 
0.2%
Other values (91) 9593
 
2.6%
ValueCountFrequency (%)
0 347118
94.9%
1 1745
 
0.5%
2 1278
 
0.3%
3 1159
 
0.3%
4 1033
 
0.3%
5 935
 
0.3%
6 869
 
0.2%
7 709
 
0.2%
8 692
 
0.2%
9 657
 
0.2%
ValueCountFrequency (%)
124 1
 
< 0.1%
99 47
< 0.1%
98 9
 
< 0.1%
97 4
 
< 0.1%
96 2
 
< 0.1%
95 4
 
< 0.1%
94 4
 
< 0.1%
93 5
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%

SCH_S
Real number (ℝ)

High correlation  Zeros 

Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29505615
Minimum0
Maximum99
Zeros347130
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:37.750903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7854019
Coefficient of variation (CV)9.4402434
Kurtosis320.82861
Mean0.29505615
Median Absolute Deviation (MAD)0
Skewness15.734658
Sum107928
Variance7.7584637
MonotonicityNot monotonic
2025-05-12T22:15:38.247956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347130
94.9%
1 12909
 
3.5%
2 366
 
0.1%
4 350
 
0.1%
3 339
 
0.1%
6 304
 
0.1%
5 292
 
0.1%
8 233
 
0.1%
9 228
 
0.1%
10 222
 
0.1%
Other values (85) 3415
 
0.9%
ValueCountFrequency (%)
0 347130
94.9%
1 12909
 
3.5%
2 366
 
0.1%
3 339
 
0.1%
4 350
 
0.1%
5 292
 
0.1%
6 304
 
0.1%
7 219
 
0.1%
8 233
 
0.1%
9 228
 
0.1%
ValueCountFrequency (%)
99 10
< 0.1%
98 3
 
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 3
 
< 0.1%
92 1
 
< 0.1%
90 2
 
< 0.1%
89 4
 
< 0.1%
88 1
 
< 0.1%

SCH_R
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
1
276081 
0
89707 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters365788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Length

2025-05-12T22:15:38.600418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T22:15:39.099939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring characters

ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Rank
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7072567
Minimum0
Maximum99
Zeros70737
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:39.398241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q313
95-th percentile41
Maximum99
Range99
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.644887
Coefficient of variation (CV)1.5086535
Kurtosis6.7339523
Mean9.7072567
Median Absolute Deviation (MAD)3
Skewness2.3951015
Sum3550798
Variance214.4727
MonotonicityNot monotonic
2025-05-12T22:15:39.802943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 95159
26.0%
0 70737
19.3%
3 13506
 
3.7%
2 12797
 
3.5%
5 11285
 
3.1%
4 10533
 
2.9%
7 9693
 
2.6%
6 9009
 
2.5%
9 8567
 
2.3%
8 7746
 
2.1%
Other values (90) 116756
31.9%
ValueCountFrequency (%)
0 70737
19.3%
1 95159
26.0%
2 12797
 
3.5%
3 13506
 
3.7%
4 10533
 
2.9%
5 11285
 
3.1%
6 9009
 
2.5%
7 9693
 
2.6%
8 7746
 
2.1%
9 8567
 
2.3%
ValueCountFrequency (%)
99 39
< 0.1%
98 49
< 0.1%
97 61
< 0.1%
96 50
< 0.1%
95 63
< 0.1%
94 63
< 0.1%
93 78
< 0.1%
92 73
< 0.1%
91 80
< 0.1%
90 80
< 0.1%

DATA_S
Real number (ℝ)

High correlation  Zeros 

Distinct192
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.274334
Minimum0
Maximum241
Zeros58829
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:40.163889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median36
Q362
95-th percentile131
Maximum241
Range241
Interquartile range (IQR)49

Descriptive statistics

Standard deviation42.675699
Coefficient of variation (CV)0.94260248
Kurtosis3.0435337
Mean45.274334
Median Absolute Deviation (MAD)23
Skewness1.5520597
Sum16560808
Variance1821.2153
MonotonicityNot monotonic
2025-05-12T22:15:40.585458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58829
 
16.1%
13 36422
 
10.0%
85 7355
 
2.0%
55 6899
 
1.9%
65 6797
 
1.9%
76 6754
 
1.8%
62 6679
 
1.8%
72 6677
 
1.8%
57 6408
 
1.8%
80 6385
 
1.7%
Other values (182) 216583
59.2%
ValueCountFrequency (%)
0 58829
16.1%
1 17
 
< 0.1%
2 259
 
0.1%
3 304
 
0.1%
4 375
 
0.1%
5 503
 
0.1%
6 558
 
0.2%
7 480
 
0.1%
8 440
 
0.1%
9 363
 
0.1%
ValueCountFrequency (%)
241 1641
0.4%
240 1
 
< 0.1%
237 1
 
< 0.1%
235 1
 
< 0.1%
229 2
 
< 0.1%
226 1
 
< 0.1%
220 1
 
< 0.1%
206 2429
0.7%
205 1
 
< 0.1%
204 1
 
< 0.1%

DATA_R
Real number (ℝ)

High correlation  Zeros 

Distinct1345
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.145751
Minimum0
Maximum1496
Zeros314028
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:41.414180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile609
Maximum1496
Range1496
Interquartile range (IQR)0

Descriptive statistics

Standard deviation228.64922
Coefficient of variation (CV)3.169268
Kurtosis13.054269
Mean72.145751
Median Absolute Deviation (MAD)0
Skewness3.6474665
Sum26390050
Variance52280.465
MonotonicityNot monotonic
2025-05-12T22:15:41.866823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 314028
85.8%
117 5999
 
1.6%
351 3217
 
0.9%
377 2772
 
0.8%
91 1647
 
0.5%
241 1429
 
0.4%
78 1342
 
0.4%
104 1293
 
0.4%
611 903
 
0.2%
412 724
 
0.2%
Other values (1335) 32434
 
8.9%
ValueCountFrequency (%)
0 314028
85.8%
1 140
 
< 0.1%
2 5
 
< 0.1%
3 2
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
1496 1
 
< 0.1%
1494 4
 
< 0.1%
1493 2
 
< 0.1%
1492 10
< 0.1%
1491 6
< 0.1%
1490 1
 
< 0.1%
1489 3
 
< 0.1%
1488 2
 
< 0.1%
1487 5
< 0.1%
1486 5
< 0.1%

Data_Sent_To_BS
Real number (ℝ)

High correlation  Zeros 

Distinct237
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4941059
Minimum0
Maximum241
Zeros303545
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:42.224660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum241
Range241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.730293
Coefficient of variation (CV)4.3902599
Kurtosis72.090852
Mean4.4941059
Median Absolute Deviation (MAD)0
Skewness7.8497284
Sum1643890
Variance389.28446
MonotonicityNot monotonic
2025-05-12T22:15:42.574225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 303545
83.0%
13 38186
 
10.4%
1 2866
 
0.8%
2 1769
 
0.5%
3 1113
 
0.3%
6 764
 
0.2%
4 761
 
0.2%
5 729
 
0.2%
8 696
 
0.2%
7 635
 
0.2%
Other values (227) 14724
 
4.0%
ValueCountFrequency (%)
0 303545
83.0%
1 2866
 
0.8%
2 1769
 
0.5%
3 1113
 
0.3%
4 761
 
0.2%
5 729
 
0.2%
6 764
 
0.2%
7 635
 
0.2%
8 696
 
0.2%
9 571
 
0.2%
ValueCountFrequency (%)
241 447
0.1%
240 85
 
< 0.1%
239 42
 
< 0.1%
238 32
 
< 0.1%
237 13
 
< 0.1%
236 15
 
< 0.1%
235 14
 
< 0.1%
234 9
 
< 0.1%
233 4
 
< 0.1%
232 12
 
< 0.1%

dist_CH_To_BS
Real number (ℝ)

High correlation  Zeros 

Distinct305
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.580986
Minimum0
Maximum201.93494
Zeros303545
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:42.932261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile145.08942
Maximum201.93494
Range201.93494
Interquartile range (IQR)0

Descriptive statistics

Standard deviation49.306364
Coefficient of variation (CV)2.2847132
Kurtosis2.4268386
Mean21.580986
Median Absolute Deviation (MAD)0
Skewness2.0069991
Sum7894065.8
Variance2431.1175
MonotonicityNot monotonic
2025-05-12T22:15:43.328510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 303545
83.0%
136.03878 1454
 
0.4%
159.31297 1446
 
0.4%
88.47785 1201
 
0.3%
93.93772 1131
 
0.3%
165.46205 1105
 
0.3%
123.96292 1031
 
0.3%
102.66424 995
 
0.3%
201.93494 983
 
0.3%
54.93262 957
 
0.3%
Other values (295) 51940
 
14.2%
ValueCountFrequency (%)
0 303545
83.0%
54.93262 957
 
0.3%
75.52972 235
 
0.1%
76.04676 96
 
< 0.1%
77.63787 76
 
< 0.1%
77.82286 61
 
< 0.1%
78.70375 64
 
< 0.1%
78.91449 223
 
0.1%
79.19069 228
 
0.1%
79.55137 82
 
< 0.1%
ValueCountFrequency (%)
201.93494 983
0.3%
181.31284 948
0.3%
176.98899 75
 
< 0.1%
176.62353 224
 
0.1%
176.42103 58
 
< 0.1%
176.40744 512
0.1%
176.34359 62
 
< 0.1%
175.01596 69
 
< 0.1%
174.29523 63
 
< 0.1%
173.64043 225
 
0.1%

send_code
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5156402
Minimum0
Maximum15
Zeros77949
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:43.685145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.397266
Coefficient of variation (CV)0.95294469
Kurtosis3.0981937
Mean2.5156402
Median Absolute Deviation (MAD)1
Skewness1.4483411
Sum920191
Variance5.7468842
MonotonicityNot monotonic
2025-05-12T22:15:43.984605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 77949
21.3%
1 74193
20.3%
2 62069
17.0%
3 49542
13.5%
4 37423
10.2%
5 25480
 
7.0%
6 15731
 
4.3%
7 10147
 
2.8%
8 5095
 
1.4%
9 2531
 
0.7%
Other values (6) 5628
 
1.5%
ValueCountFrequency (%)
0 77949
21.3%
1 74193
20.3%
2 62069
17.0%
3 49542
13.5%
4 37423
10.2%
5 25480
 
7.0%
6 15731
 
4.3%
7 10147
 
2.8%
8 5095
 
1.4%
9 2531
 
0.7%
ValueCountFrequency (%)
15 595
 
0.2%
14 628
 
0.2%
13 884
 
0.2%
12 836
 
0.2%
11 1097
 
0.3%
10 1588
 
0.4%
9 2531
 
0.7%
8 5095
 
1.4%
7 10147
2.8%
6 15731
4.3%

Expaned Energy
Real number (ℝ)

Skewed 

Distinct69352
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30334516
Minimum0
Maximum45.09394
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-05-12T22:15:44.371709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00739
Q10.05598
median0.09755
Q30.21703
95-th percentile1.6656
Maximum45.09394
Range45.09394
Interquartile range (IQR)0.16105

Descriptive statistics

Standard deviation0.67209017
Coefficient of variation (CV)2.2155955
Kurtosis1613.5145
Mean0.30334516
Median Absolute Deviation (MAD)0.053175
Skewness25.699348
Sum110960.02
Variance0.45170519
MonotonicityNot monotonic
2025-05-12T22:15:44.749186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00447 1201
 
0.3%
0.00448 874
 
0.2%
0.00446 657
 
0.2%
0.00606 404
 
0.1%
0.2194 390
 
0.1%
0.00607 382
 
0.1%
0.00722 364
 
0.1%
0.00723 351
 
0.1%
0.00449 332
 
0.1%
0.21826 323
 
0.1%
Other values (69342) 360510
98.6%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.00093 8
 
< 0.1%
0.00108 62
< 0.1%
0.00167 1
 
< 0.1%
0.00168 15
 
< 0.1%
0.00169 27
< 0.1%
0.0017 12
 
< 0.1%
0.00171 13
 
< 0.1%
0.00172 23
 
< 0.1%
0.00173 22
 
< 0.1%
ValueCountFrequency (%)
45.09394 1
< 0.1%
45.09063 1
< 0.1%
45.07812 1
< 0.1%
45.07668 1
< 0.1%
45.07618 1
< 0.1%
45.07489 1
< 0.1%
45.0745 1
< 0.1%
45.07431 1
< 0.1%
45.07407 1
< 0.1%
45.07405 1
< 0.1%

Attack type
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.0 MiB
Normal
332040 
Grayhole
 
13909
Blackhole
 
10049
TDMA
 
6633
Flooding
 
3157

Length

Max length9
Median length6
Mean length6.1394606
Min length4

Characters and Unicode

Total characters2245741
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 332040
90.8%
Grayhole 13909
 
3.8%
Blackhole 10049
 
2.7%
TDMA 6633
 
1.8%
Flooding 3157
 
0.9%

Length

2025-05-12T22:15:45.134020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T22:15:45.433204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 332040
90.8%
grayhole 13909
 
3.8%
blackhole 10049
 
2.7%
tdma 6633
 
1.8%
flooding 3157
 
0.9%

Most occurring characters

ValueCountFrequency (%)
l 369204
16.4%
o 362312
16.1%
a 355998
15.9%
r 345949
15.4%
N 332040
14.8%
m 332040
14.8%
h 23958
 
1.1%
e 23958
 
1.1%
G 13909
 
0.6%
y 13909
 
0.6%
Other values (12) 72464
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2245741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 369204
16.4%
o 362312
16.1%
a 355998
15.9%
r 345949
15.4%
N 332040
14.8%
m 332040
14.8%
h 23958
 
1.1%
e 23958
 
1.1%
G 13909
 
0.6%
y 13909
 
0.6%
Other values (12) 72464
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2245741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 369204
16.4%
o 362312
16.1%
a 355998
15.9%
r 345949
15.4%
N 332040
14.8%
m 332040
14.8%
h 23958
 
1.1%
e 23958
 
1.1%
G 13909
 
0.6%
y 13909
 
0.6%
Other values (12) 72464
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2245741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 369204
16.4%
o 362312
16.1%
a 355998
15.9%
r 345949
15.4%
N 332040
14.8%
m 332040
14.8%
h 23958
 
1.1%
e 23958
 
1.1%
G 13909
 
0.6%
y 13909
 
0.6%
Other values (12) 72464
 
3.2%

Interactions

2025-05-12T22:15:20.962270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:13:59.697340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:06.281032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:11.611609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:17.728920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:23.292890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:29.153939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:34.890583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:40.421147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:46.354057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:51.305017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:56.797600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:03.957699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:09.255590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:14.953319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:21.422005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:00.240484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:06.656209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:11.955242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:18.117410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:23.706139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:29.646181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:35.311135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:41.217877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:46.689131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:51.671559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:57.180903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-05-12T22:15:21.800112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:00.619797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:07.070266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:12.311286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:18.595370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:24.062314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:30.099515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:35.726750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:41.641574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:47.068368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:51.998958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:57.550793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-05-12T22:14:41.963730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-05-12T22:15:05.024964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-05-12T22:14:07.776787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:13.045921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:19.296951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:24.819142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:30.932723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:36.447254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:42.628441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:47.712039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:52.803368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:58.281226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:05.391479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:10.777069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:16.516296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:23.122244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:01.714851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:08.123321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:13.660419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:19.668610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:25.221692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:31.270554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:36.886216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:42.925589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:48.022200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:53.205681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:58.639940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:05.717658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:11.109159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:16.891249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:23.465708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:02.045556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:08.454185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:14.128213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:19.965777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:25.566627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:31.591824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:37.316083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:43.247328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:48.324312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:53.553899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:58.992791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:06.099805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:11.502988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:17.230714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:23.803541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:02.366192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:08.823739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:14.679436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:20.286158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:25.977618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:31.913083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:37.677013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:43.563105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:48.614600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:54.017122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:59.350569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:06.448729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:11.859092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:17.648894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:24.162841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:02.963513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:09.185862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:15.026105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:20.737690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:26.717738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:32.299143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:37.980721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:43.887316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:48.906777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:54.367719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:59.714840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:06.774549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:12.240786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:18.105107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:24.483690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:03.293884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:09.512008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:15.390941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:21.132359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:27.076436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:32.642872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:38.287822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:44.184558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:49.265867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:54.703671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:00.167775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:07.190387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:12.591622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:18.677659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:24.811852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:04.105046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:09.836184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:15.723704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:21.503988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:27.381910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:33.033672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:38.631580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:44.512549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:49.584346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:55.039337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:01.427625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:07.507384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:12.941453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:19.029467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:25.197175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:04.707475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:10.228649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:16.087210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:21.891755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:27.753046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:33.482289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:38.958090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:44.865876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:49.900991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:55.417431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:02.026765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:07.840101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:13.353557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:19.438960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:26.403377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:05.068089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:10.557627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:16.492171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:22.224683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:28.090222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:33.834472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:39.288052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:45.242295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:50.262129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:55.742496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:02.734173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:08.211266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:13.708218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:19.795518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:26.755072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:05.406443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:10.905953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:16.863116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:22.577413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:28.478955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:34.193037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:39.745456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:45.612912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:50.606364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:56.101854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:03.173487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:08.560350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:14.077728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:20.177152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:27.150908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:05.845382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:11.291894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:17.257895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:22.972039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:28.833798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:34.535649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:40.092069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:46.053730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:50.922228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:14:56.478209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:03.578018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:08.902002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:14.465607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-12T22:15:20.562447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-12T22:15:45.725843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ADV_RADV_SDATA_RDATA_SData_Sent_To_BSDist_To_CHIs_CHJOIN_RJOIN_SSCH_RSCH_STimedist_CH_To_BSidsend_codewho CHAttack typeExpaned EnergyRank
ADV_R1.0000.277-0.3210.177-0.232-0.0580.4270.0080.2550.3730.010-0.052-0.227-0.0540.309-0.0530.3100.071-0.023
ADV_S0.2771.0000.217-0.5270.343-0.4950.2350.6350.1630.1070.6360.2340.3330.263-0.5000.2630.419-0.047-0.512
DATA_R-0.3210.2171.000-0.4610.783-0.5240.3240.4180.6210.5610.4120.2560.7660.301-0.5430.3030.1220.450-0.426
DATA_S0.177-0.527-0.4611.000-0.4580.2430.468-0.3380.6730.737-0.338-0.115-0.460-0.1670.601-0.1680.2030.2480.231
Data_Sent_To_BS-0.2320.3430.783-0.4581.000-0.5570.3830.2620.2580.2320.2580.3220.9900.387-0.5560.3890.1410.469-0.475
Dist_To_CH-0.058-0.495-0.5240.243-0.5571.0000.324-0.3170.4660.417-0.317-0.374-0.558-0.4150.426-0.4170.140-0.3530.558
Is_CH0.4270.2350.3240.4680.3830.3241.0000.4060.6960.6130.2680.2980.3330.3540.4290.3540.8680.0090.248
JOIN_R0.0080.6350.418-0.3380.262-0.3170.4061.0000.2820.2580.9990.0190.2190.011-0.3200.0110.2120.086-0.328
JOIN_S0.2550.1630.6210.6730.2580.4660.6960.2821.0000.8960.1870.5000.7830.6210.6170.6210.6040.0080.318
SCH_R0.3730.1070.5610.7370.2320.4170.6130.2580.8961.0000.1700.4410.6970.5630.5410.5630.5320.0070.266
SCH_S0.0100.6360.412-0.3380.258-0.3170.2680.9990.1870.1701.0000.0220.2160.014-0.3200.0140.3570.082-0.328
Time-0.0520.2340.256-0.1150.322-0.3740.2980.0190.5000.4410.0221.0000.3320.983-0.2800.9830.1950.169-0.454
dist_CH_To_BS-0.2270.3330.766-0.4600.990-0.5580.3330.2190.7830.6970.2160.3321.0000.399-0.5580.4000.1710.452-0.470
id-0.0540.2630.301-0.1670.387-0.4150.3540.0110.6210.5630.0140.9830.3991.000-0.3230.9990.2140.199-0.492
send_code0.309-0.500-0.5430.601-0.5560.4260.429-0.3200.6170.541-0.320-0.280-0.558-0.3231.000-0.3240.186-0.0410.397
who CH-0.0530.2630.303-0.1680.389-0.4170.3540.0110.6210.5630.0140.9830.4000.999-0.3241.0000.2140.201-0.493
Attack type0.3100.4190.1220.2030.1410.1400.8680.2120.6040.5320.3570.1950.1710.2140.1860.2141.0000.0190.107
Expaned Energy0.071-0.0470.4500.2480.469-0.3530.0090.0860.0080.0070.0820.1690.4520.199-0.0410.2010.0191.000-0.281
Rank-0.023-0.512-0.4260.231-0.4750.5580.248-0.3280.3180.266-0.328-0.454-0.470-0.4920.397-0.4930.107-0.2811.000

Missing values

2025-05-12T22:15:27.529331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-12T22:15:28.724526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idTimeIs_CHwho CHDist_To_CHADV_SADV_RJOIN_SJOIN_RSCH_SSCH_RRankDATA_SDATA_RData_Sent_To_BSdist_CH_To_BSsend_codeExpaned EnergyAttack type
01010005011010000.00000100251000120048130.0853502.46940Normal
110100150010104475.32345041001238000.0000040.06957Normal
210100250010101046.954530410011941000.0000030.06898Normal
310100350010104464.852310410011638000.0000040.06673Normal
41010045001010104.833410410012541000.0000030.06534Normal
510100550010101031.911980410011841000.0000030.06717Normal
610100650010104424.34167041001538000.0000040.06214Normal
710100750010101026.750330410012141000.0000030.06662Normal
810100850010104463.664850410011738000.0000040.06649Normal
910100950010100032.902170410011248000.0000010.07903Normal
idTimeIs_CHwho CHDist_To_CHADV_SADV_RJOIN_SJOIN_RSCH_SSCH_RRankDATA_SDATA_RData_Sent_To_BSdist_CH_To_BSsend_codeExpaned EnergyAttack type
3746512010911003020103736.4350405100163902382.3893320.07061Normal
3746522010921003020106323.85398051001196038149.3991940.16273Normal
3746532010931003020109518.812690510011355032124.7845910.09477Normal
3746542010941003020100417.164000510011857043166.8939750.09722Normal
374655201095100312010950.00000140211000112022124.2017001.01807Normal
374656201096100302010516.98337051001796067170.1477930.15974Normal
3746572010971003020103729.32867051001313902482.2104320.06877Normal
3746582010981003020109518.519630510011755031139.2643810.09437Normal
374659201099100302010518.55001051001396065158.2749230.16047Normal
374660202041102502021000.00000050000476897115.0040701.01325Normal